Permanent Ice and Snow

Transcription

1 Soil Moisture Active Passive (SMAP) Ancillary Data Report Permanent Ice and Snow Preliminary, v.1 SMAP Science Document no. 048 Kyle McDonald, E. Podest, E. Njoku Jet Propulsion Laboratory California Institute of Technology Pasadena, CA January 11, 2013 JPL D Jet Propulsion Laboratory California Institute of Technology 2013 California Institute of Technology. Government sponsorship acknowledged.

2 Preface The SMAP Ancillary Data Reports provide descriptions of ancillary data sets used with the science algorithm software in generation of the SMAP science data products. The Ancillary Data Reports may undergo additional updates as new ancillary data sets or processing methods become available. The most recent versions of the ancillary data reports will be made available, along with the Algorithm Theoretical Basis Documents (ATBDs), at the SMAP web site i

3 Table of Contents Preface... i 1 Overview Purpose Requirement Candidate Datasets Dataset Selection and Description Processing Acknowledgment References... 3 Appendix A: SMAP Science Data Products and ATBDs... 4 Appendix B: SMAP Ancillary Data Reports... 5 ii

4 1 Overview 1.1 Purpose The purpose of this report is to evaluate options and select a permanent ice and snow dataset to support generation of the SMAP science data products. The permanent ice and snow dataset is one of a suite of ancillary datasets required by the SMAP science processing algorithms. The algorithms and ancillary data are described in SMAP algorithm theoretical basis documents (ATBDs) and ancillary data reports. The ATBDs and ancillary data reports are listed in Appendices A and B and are available at the SMAP web site: Requirement The permanent ice and snow dataset is applied within the L2 and L3 soil moisture and freeze/thaw algorithms to provide a spatial mask that indicates areas of permanent ice and snow within a sensor footprint or retrieval grid cell. The permanent ice and snow regions correspond to areas where the freeze/thaw product would not be useful for characterizing soil moisture or seasonal freeze/thaw transitions, and the associated geophysical retrievals would not be relevant to SMAP mission objectives. 2 Candidate Datasets Candidate datasets defining permanent ice and snow regions have been identified by the SMAP team and are listed in Table 1. The datasets listed are global in extent and are characterized by their source data (e.g., MODIS) and classification scheme (e.g., IGBP). The classification schemes for the MODIS and SPOT datasets are subsets of the IGBP scheme. The ECOCLIMAP (Masson et al., 2003) dataset is used by the ECMWF forecast model and the SMOS (Soil Moisture Ocean Salinity) mission soil moisture retrieval. The ECOCLIMAP provides 215 classes within Europe and 17 classes for the rest of the world. The sources of the European classification data are CORINE and PELCOM. Outside of Europe, the classification schemes of the Pan-European Land Cover Monitoring UMD and IGBP were used in combination. The Global Land Cover Characteristics Data Base provided by the USGS is derived using one year of AVHRR data. Dataset Name MODIS_IGBP MCD12Q1 Table 1. List of the candidate datasets. Spatial Resolution 500 m Temporal Resolution Annual SPOT_IGBP 1 km ECOCLIMAP 1 km One time Global Land Cover Characteristics Data Base (USGS) 1km Seasonal

5 3 Dataset Selection and Description Based on assessment of the characteristics of the datasets in Table 1, the permanent ice landcover class component of the MODIS MCD12Q1 IGPB classification was determined to be the most suitable dataset for application to SMAP processing. This assessment was based on the product s higher spatial resolution (500m), annual update, and widespread utilization within the Earth science community as a standard land cover product. Selection of this dataset also provides internal project consistency with other SMAP processing needs, since the MODIS landcover product was also identified as the landcover dataset of choice for use in the SMAP L2 soil moisture algorithms. The MODIS Land Cover Type product contains multiple landcover classes that describe land cover properties derived from observations spanning a single year s input of Terra and Aqua data. The primary land cover scheme identifies 17 land cover classes defined by the IGBP, which includes 11 natural vegetation classes, 3 developed and mosaicked land classes, and three nonvegetated land classes which include the permanent ice and snow class, defined as lands under snow and ice throughout the year. It is identified as class #15 in the MODIS_IGBP classification product and covers 11.04% of the globe. Figure 1 shows the MODIS IGPB classification. Figure 1. The MODIS IGBP classification. Permenant ice corresponds to class 15, delineated in white. ( The annually updated MODIS-IGBP files at 500 m resolution are available through the NASA DAAC at The dataset has the following characteristics: Collection: MODIS (MCD12Q1) LAND COVER Native Projection: WGS 84 (EPSG:4326) Spatial Extent: N: 90.0, S: -90.0, E: 180.0, W: Updates: Updated annually, datasets from Map Units: degrees 2

6 Resolution: 500 meters Earth-Gridded Tile Area: ~1200 x 1200 km (~10 x 10 at the equator) Image Dimensions: 2400 x 2400 rows/columns File Size: ~88 MB Projection: Sinusoidal Data type: 8-bit unsigned integer Data Format: HDF-EOS 4 Processing The annual classification files were produced at 1, 3, 9, and 36 km resolutions by determining the dominant percent permanent ice and snow within each resolution. The outputs were projected to the EASE grid coordinates. Figure 1 shows an example map of the MODIS IGBP classification, which includes permanent snow and ice as a landcover class. 5 Acknowledgment This work was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. 6 References Belward, A. E. (1996): The IGBP-DIS global 1 km land cover data set "DISCover" - Proposal and implementation plans. Report of the Land Cover Working Group of the IGBP-DIS. IGBP-DIS Working Paper, No. 13. Stockholm., 63 pp. Crow, W. T., and co-authors (2005): An observing system simulation experiment for Hydros ramiometer-only soil moisture products, IEEE Trans. Geosci. Remote Sens., 43, Kim, S. B., and E. G. Njoku (2010): Soil moisture retrieval using data cube representation of radar scattering, Progress In Electromagnetic Research Symposium (PIERS) 2010, Cambridge MA, pp. Masson, V., J. L. Champeaux, F. Chauvin, C. Meriguet, and R. Lacaze (2003): A global database of land surface parameters at 1-km resolution in meteorological and climate models, Journal of Climate, 16, Strahler, A., D. Muchoney, J. Borak, F. Friedl, S. Gopal, L. Lambin, and A. Moody (1999): MODIS Land Cover Product Algorithm Theoretical Basis Document (ATBD), 72 pp. 3

7 Appendix A: SMAP Science Data Products and ATBDs The SMAP Algorithm Theoretical Basis Documents are available at the SMAP web site Data Product Description ATBD L1A_Radar L1A_Radiometer L1B_S0_LoRes L1C_S0_HiRes L1B_TB L1C_TB L2_SM_A L2_SM_P L2_SM_AP L3_FT_A L3_SM_A L3_SM_P L3_SM_AP L4_SM L4_C Radar raw data in time order Radiometer raw data in time order Low resolution radar σ o in time order High resolution radar σ o (half orbit, gridded) Radiometer T B in time order Radiometer T B (half orbit, gridded) Soil moisture (radar, half orbit) Soil moisture (radiometer, half orbit) Soil moisture (radar/radiometer, half orbit) Freeze/thaw state (radar, daily composite) Soil moisture (radar, daily composite) Soil moisture (radiometer, daily composite) Soil moisture (radar/radiometer, daily composite) Soil moisture (surface & root zone) Carbon net ecosystem exchange (NEE) (Joint with L1C_S0_HiRes) (Joint with L1B_TB) (Joint with L1C_S0_HiRes) West, R., L1B & L1C radar products, JPL D-53052, JPL, Pasadena, CA. Piepmeier, J. et al., L1B radiometer product, GSFC SMAP-006, GSFC, Greenbelt, MD. Chan, S. et al., L1C radiometer product, JPL D , JPL, Pasadena, CA. Kim, S. et al., L2 & L3 radar soil moisture (active) product, JPL D-66479, JPL, Pasadena, CA. O Neill, P. et al., L2 & L3 radiometer soil moisture (passive) product, JPL D-66480, JPL, Pasadena, CA. Entekhabi, D. et al., L2 & L3 radar/radiometer soil moisture (active/passive) products, JPL D-66481, JPL, Pasadena, CA. McDonald, K. et al., L3 radar freeze/thaw (active) product, JPL D-66482, JPL, Pasadena, CA. (Joint with L2_SM_A) (Joint with L2_SM_P) (Joint with L2_SM_AP) Reichle, R. et al., L4 surface and root-zone soil moisture product, JPL D-66483, JPL, Pasadena, CA. Kimball, J. et al., L4 carbon product, JPL D-66484, JPL, Pasadena, CA. 4

8 Appendix B: SMAP Ancillary Data Reports The SMAP Ancillary Data Reports are available with the ATBDs at the SMAP web site Crop Type Data/Parameter Digital Elevation Model Landcover Classification Soil Attributes Static Water Fraction Urban Area Vegetation Water Content Permanent Ice Precipitation Snow Surface Temperature Vegetation and Roughness Parameters Ancillary Data Report Kim, S., Crop Type, JPL D-53054, Pasadena, CA Podest, E. et al., Digital Elevation Model, JPL D-53056, Pasadena, CA Kim, S., Landcover Classification, JPL D-53057, Pasadena, CA Das, N. et al., Soil Attributes, JPL D-53058, Pasadena, CA Chan, S. et al., Static Water Fraction, JPL D-53059, Pasadena, CA Das, N., Urban Area, JPL D-53060, Pasadena, CA Chan, S. et al., Vegetation Water Content, JPL D-53061, Pasadena, CA McDonald, K., Permanent Ice & Snow, JPL D-53062, Pasadena, CA Dunbar, S., Precipitation, JPL D-53063, Pasadena, CA Kim, E. et al., Snow, GSFC SMAP-007, Greenbelt, MD Fisher, J. et al., Surface Temperature, JPL D Pasadena, CA Colliander, A., Vegetation & Roughness Parameters, JPL D-53065, Pasadena, CA 5